Skripsi

PERBANDINGAN NAÏVE BAYES, SVM, K-NN, DECISION TREE DAN RANDOM FOREST PADA ANALISIS SENTIMEN BERBASIS ASPEK APLIKASI SEABANK

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The increasing use of digital banking applications has led to the need for a deeper understanding of user perceptions, especially through aspect-based sentiment analysis. This study aims to classify the sentiment of SeaBank app users by focusing on four main aspects: learnability, efficiency, technical issues or errors, and satisfaction. Review data totaling 1,971 comments were collected from the Google Play Store and labeled with sentiments based on the scores (ratings) given by users. The CRISP-DM approach serves as the methodological framework for this study, which includes five classification algorithms: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, and Random Forest. The evaluation results show that the SVM algorithm provides the best performance with the highest average value of the four aspects achieving accuracy of 93.91%,Precision of 91.16%, recall of 97.96% and F1-Measure of 94.33%. According to the research findings, the Support Vector Machine (SVM) algorithm provides the best performance when performing aspect-based sentiment analysis on text data from digital banking application reviews. The findings are expected to serve as a reference for the development of automated evaluation systems that rely on user opinions as the basis for decision making. Keywords: aspects; CRISP-DM; digital Banking; seabank; sentiment analysis

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Detail Information

Series Title

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Call Number

T1895492025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 104 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.307

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Program Aplikasi Komputer
Prodi Sistem Informasi

Specific Detail Info

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Statement of Responsibility

MI

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